Weighted sum rate maximization in the underlay cognitive MISO Interference Channel

Gallo, Laurent; Negro, Francesco; Ghauri, Irfan; Slock, Dirk

doi:10.1109/pimrc.2011.6140045

Cited by 11 publications

(20 citation statements)

References 15 publications

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“…Even though this assumption is an ideal hypothesis, the problem of imperfect CSI can be neglected with the purpose of investigating the potential of new transmission schemes combining resource allocation and precoding techniques. The perfect CSI assumption has also been considered in recent contributions, including [7], [9], [15]- [17] in order to ease studies of beamforming design for CR-MU-MIMO networks.…”

Section: Kmentioning

confidence: 99%

Efficient ZF-WF strategy for sum-rate maximization of MU-MISO cognitive radio networks

Claudino

Abrão

2018

AEU - International Journal of Electronics and Communications

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This article presents an efficient quasi-optimal sum rate (SR) maximization technique based on zero-forcing water-filling (ZFWF) algorithm directly applied to cognitive radio networks (CRNs). We have defined the non-convexity nature of the optimization problem in the context of CRNs while we have offered all necessary conditions to solve the related SR maximization problem, which considers power limit at cognitive transmitter and interference levels at primary users (PUs) and secondary users (SUs).A general expression capable to determine the optimal number of users as a function of the main system parameters, namely the signal-to-interference-plus-noise ratio (SINR) and the number of BS antennas is proposed. Our numerical results for the CRN performance are analyzed in terms of both BER and sum-capacity for the proposed ZF-WF precoding technique, and compared to the classical minimum mean square error (MMSE), corroborating the effectiveness of the proposed technique operating in multi user multiple input single output (MU-MISO) CRNs.

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Section: Kmentioning

confidence: 99%

Efficient ZF-WF strategy for sum-rate maximization of MU-MISO cognitive radio networks

Claudino

Abrão

2018

AEU - International Journal of Electronics and Communications

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“…In [17] a MIMO cognitive radio system with multiple secondary data streams is considered, and beamforming schemes for transmit power minimization subject to individual signal-to-noise ratio (SNR) requirements for each data stream are proposed. Further contributions in MIMO underlay cognitive radio systems are provided in [18]- [20], where different resource allocation algorithms for rate optimization are proposed. In [21] a MIMO device-to-device (D2D) underlay network is analyzed, and resource allocation algorithms for sum rate maximization are developed, using a pricing-based approach.…”

Section: Arxiv:150908309v2 [Csit] 28 Oct 2016mentioning

confidence: 99%

“…We observe that computing (18) can be managed with affordable complexity, since Problem (19) has a convex feasibility set, while the objective has a concave numerator and an affine denominator. Thus, (19) can be globally solved with polynomial complexity by means of fractional programming theory.…”

Section: B Resource Allocation Algorithmmentioning

confidence: 99%

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Energy Efficiency in MIMO Underlay and Overlay Device-to-Device Communications and Cognitive Radio Systems

Zappone

Matthiesen

Jorswieck

2017

IEEE Trans. Signal Process.

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Abstract-This paper addresses the problem of resource allocation for systems in which a primary and a secondary link share the available spectrum by an underlay or overlay approach. After observing that such a scenario models both cognitive radio and D2D communications, we formulate the problem as the maximization of the secondary energy efficiency subject to a minimum rate requirement for the primary user. This leads to challenging non-convex, fractional problems. In the underlay scenario, we obtain the global solution by means of a suitable reformulation. In the overlay scenario, two algorithms are proposed. The first one yields a resource allocation fulfilling the first-order optimality conditions of the resource allocation problem, by solving a sequence of easier fractional problems. The second one enjoys a weaker optimality claim, but an even lower computational complexity. Numerical results demonstrate the merits of the proposed algorithms both in terms of energyefficient performance and complexity, also showing that the two proposed algorithms for the overlay scenario perform very similarly, despite the different complexity.

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“…We further assume that the channel matrices can be perfectly known at the secondary BS, using a genie added feedback. Though this assumption is quite ideal, it has been considered in [28], [29], [32] to study their problems of interest in the context of CR networks. In reality, perfect channel estimation is hardly achieved and thus the results obtained in this paper may act as an upper bound on the SR performance for the secondary transmission in an underlay CR network.…”

Section: System Model and Problem Formulationmentioning

confidence: 99%

An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

Nguyen

Tran

Shin

et al. 2016

IEEE Trans. Veh. Technol.

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Abstract-We consider a linear precoder design for an underlay cognitive radio multiple-input multiple-output broadcast channel, where the secondary system consisting of a secondary base-station (BS) and a group of secondary users (SUs) is allowed to share the same spectrum with the primary system. All the transceivers are equipped with multiple antennas, each of which has its own maximum power constraint. Assuming zero-forcing method to eliminate the multiuser interference, we study the sum rate maximization problem for the secondary system subject to both per-antenna power constraints at the secondary BS and the interference power constraints at the primary users. The problem of interest differs from the ones studied previously that often assumed a sum power constraint and/or single antenna employed at either both the primary and secondary receivers or the primary receivers. To develop an efficient numerical algorithm, we first invoke the rank relaxation method to transform the considered problem into a convex-concave problem based on a downlink-uplink result. We then propose a barrier interior-point method to solve the resulting saddle point problem. In particular, in each iteration of the proposed method we find the Newton step by solving a system of discrete-time Sylvester equations, which help reduce the complexity significantly, compared to the conventional method. Simulation results are provided to demonstrate fast convergence and effectiveness of the proposed algorithm.

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Weighted sum rate maximization in the underlay cognitive MISO Interference Channel

Cited by 11 publications

References 15 publications

Efficient ZF-WF strategy for sum-rate maximization of MU-MISO cognitive radio networks

Efficient ZF-WF strategy for sum-rate maximization of MU-MISO cognitive radio networks

Energy Efficiency in MIMO Underlay and Overlay Device-to-Device Communications and Cognitive Radio Systems

An Efficient Precoder Design for Multiuser MIMO Cognitive Radio Networks with Interference Constraints

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